
Depending on the dataset, very simple and computationally cheap methods (such as Gradient or InputXGradient) can perform nearly as well as the computationally more expensive methods such as ExpectedGradients or DeepSHAP.

Complementarity between methods, where some methods outperform other methods on a subset of metrics and vice versa, suggests that a combination of attribution maps given by different methods might provide more information than the individual attribution maps. This is related to the idea proposed in [1]} that different metrics might be measuring different underlying aspects of the attribution maps.

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